Patents

2023

1. Systems and methods for dynamic demand sensing and forecast adjustment

   Ali Khanafer , Behrouz Haji Soleimani , Sebastien Ouellet , Christopher Wang, Chantal Bisson-Krol , and Zhen Lin

   Aug 2023

   Abs Bib HTML

   Systems and methods for dynamic demand sensing in a supply chain in which constantly-updated data is used to select a machine learning model or retrain a pre-selected machine learning model, for forecasting sales of a product at a specific location. The updated data includes product information and geographic information. Also disclosed are systems and methods relating to demand forecasting and readjusting forecasts based on forecast error.

   @patent{US-2023401592-A1,
     author = {Khanafer, Ali and Haji Soleimani, Behrouz and Ouellet, Sebastien and Wang, Christopher and Bisson-Krol, Chantal and Lin, Zhen},
     title = {Systems and methods for dynamic demand sensing and forecast adjustment},
     number = {US20230401592A1},
     year = {2023},
     month = aug,
     holder = {Kinaxis Inc.},
     type = {patentus},
   }

2022

1. Systems and methods for dynamic demand sensing

   Sebastien Ouellet , Zhen Lin , Christopher Wang, and Chantal Bisson-Krol

   Nov 2022

   Abs Bib HTML

   Systems and methods for dynamic demand sensing in a supply chain in which constantly-updated data is used to select a machine learning model or retrain a pre-selected machine learning model, for forecasting sales of a product at a specific location. The updated data includes product information and geographic information.

   @patent{US-11875367-B2,
     author = {Ouellet, Sebastien and Lin, Zhen and Wang, Christopher and Bisson-Krol, Chantal},
     title = {Systems and methods for dynamic demand sensing},
     number = {US11875367B2},
     year = {2022},
     month = nov,
     holder = {Kinaxis Inc.},
     type = {patentus},
   }

2020

1. Systems and methods for features engineering

   Sebastien Ouellet , Zhen Lin , Christopher Wang, and Chantal Bisson-Krol

   Apr 2020

   Abs Bib HTML

   Systems and methods for features engineering, in which internal and external signals are received and fused. The fusing is based on meta-data of each of the one or more internal signals and each of the one or more external signals. A set of features is generated based on one or more valid combinations that match a transformation input, the transformation forming part of library of transformations. Finally, a set of one or more features is selected from the plurality of features, based on a predictive strength of each feature. The set of selected features can be used to train and select a machine learning model.

   @patent{US-11537825-B2,
     author = {Ouellet, Sebastien and Lin, Zhen and Wang, Christopher and Bisson-Krol, Chantal},
     title = {Systems and methods for features engineering},
     number = {US11537825B2},
     year = {2020},
     month = apr,
     holder = {Kinaxis Inc.},
     type = {patentuk},
   }